senseweight

Tools for sensitivity analysis for weighted estimators

https://github.com/melodyyhuang/senseweight

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Tools for sensitivity analysis for weighted estimators

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# senseweight

[![R-CMD-check](https://github.com/melodyyhuang/senseweight/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/melodyyhuang/senseweight/actions/workflows/R-CMD-check.yaml)


`senseweight` implements a set of sensitivity functions and tools to help researchers transparently conduct sensitivity analyses for weighted estimators. `senseweight` allows researchers to assess the sensitivity present in their weighted estimates to omitted confounders. Specific methods provided in `senseweight` include the following: (1) visualization tools to summarize sensitivity; (2) summary tables containing necessary sensitivity statistics; (3) formal benchmarking methods which allow researchers to use observed covariates to assess the plausibility of different confounders. 

## Installation

You can install the development version of senseweight from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("melodyyhuang/senseweight")
```
```{r, echo=FALSE, message=FALSE}
library(ggplot2)
library(tidyverse)
ggMelody <- theme_minimal() + theme(
  plot.title = element_text(hjust = 0.5, size = 17, face = "bold"),
  axis.text = element_text(size = 9),
  legend.position = "bottom", axis.title = element_text(size = 12),
  strip.text.x = element_text(size = 12, face = "bold"),
  strip.text.y = element_text(size = 12, face = "bold"),
  plot.subtitle = element_text(size = 14, hjust = 0.5)
)
theme_set(ggMelody)
```

## References
The package implements a series of methods developed in the following papers. 

For the technical introduction of the sensitivity tools:

* [Huang, Melody. "Sensitivity Analysis in the Generalization of Experimental Results."  Journal of the Royal Statistical Society Series A: Statistics in Society (2024)](https://academic.oup.com/jrsssa/advance-article-abstract/doi/10.1093/jrsssa/qnae012/7626119)
* [Hartman, Erin and Huang, Melody. "Sensitivity Analysis for Survey Weights."  Political Analysis (2024)](https://www.cambridge.org/core/journals/political-analysis/article/sensitivity-analysis-for-survey-weights/0A13E3843155099F169CF195B8D7604F)

For less technical introductions with interesting applications and best practice: 

* Huang, Melody and Hartman, Erin. "Assessing Nonignorable Response: Sensitivity Analysis for Survey Weighting, with Applications to Survey Estimates of COVID-19 Vaccination Uptake." Working paper.
* Bailey, Michael. "Polling at a Crossroads." (Chapter 7)

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